Application of wavelet theory to power distribution systems for fault detection

Author

Momoh, James ; Rizy, D. Tom

Author_Institution

Dept. of Electr. Eng., Howard Univ., Washington, DC, USA

fYear

1996

Firstpage

345

Lastpage

350

Abstract

In this paper, an investigation of the wavelet transform as a means of creating a feature extractor for artificial neural network (ANN) training is presented for application to distribution network fault location. The study includes a terrestrial-based three-phase delta-delta power distribution system. Faults were injected into the system and data was obtained from experimentation. Graphical representations of the feature extractors obtained in the time domain, the frequency domain and the wavelet domain are presented to ascertain the superiority of the wavelet transform feature extractor

Keywords

distribution networks; fault location; feature extraction; learning (artificial intelligence); neural nets; power system analysis computing; wavelet transforms; application; artificial neural network; distribution network fault location; fault injection; feature extractor; frequency domain; graphical representation; power distribution systems; three-phase delta-delta power system; time domain; training; wavelet domain; wavelet transform; Artificial neural networks; Data mining; Electrical fault detection; Feature extraction; Fourier transforms; Impedance; Neural networks; Power distribution; Wavelet domain; Wavelet transforms;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems Applications to Power Systems, 1996. Proceedings, ISAP '96., International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-3115-X

Type

conf

DOI

10.1109/ISAP.1996.501096

Filename

501096